Single Platform Geolocation of Radio Frequency Emitters

The focus of this research is on single platform geolocation methods where the position of a single stationary radio frequency emitter is estimated from multiple simulated angle and frequency of arrival measurements taken from a single moving receiver platform. The analysis scenario considered consists of a single 6U CubeSat in low earth orbit receiving radio frequency signals from a stationary emitter located on the surface of the Earth. A multiple element receive antenna array and the multiple signal classification algorithm are used to estimate the angles of arrival of an impinging signal. The maximum likelihood estimator is used to estimate the frequency of arrival of the received signal. Four geolocation algorithms are developed and the accuracy performance is compared to the Cramer-Rao lower bounds through Monte Carlo simulations. Results from a system parameter sensitivity analysis show the combined angle and frequency of arrival geolocation maximum likelihood estimator consistently outperforms the other three geolocation algorithms

Accurate Noise Projection for Reduced Stochastic Epidemic Models

We consider a stochastic Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological model. Through the use of a normal form coordinate transform, we are able to analytically derive the stochastic center manifold along with the associated, reduced set of stochastic evolution equations. The transformation correctly projects both the dynamics and the noise onto the center manifold. Therefore, the solution of this reduced stochastic dynamical system yields excellent agreement, both in amplitude and phase, with the solution of the original stochastic system for a temporal scale that is orders of magnitude longer than the typical relaxation time. This new method allows for improved time series prediction of the number of infectious cases when modeling the spread of disease in a population. Numerical solutions of the fluctuations of the SEIR model are considered in the infinite population limit using a Langevin equation approach, as well as in a finite population simulated as a Markov process.Comment: 38 pages, 10 figures, new title, Final revision to appear in Chao

Does the Debris Disk around HD 32297 Contain Cometary Grains?

We present an adaptive optics imaging detection of the HD 32297 debris disk at L' (3.8 \microns) obtained with the LBTI/LMIRcam infrared instrument at the LBT. The disk is detected at signal-to-noise per resolution element ~ 3-7.5 from ~ 0.3-1.1" (30-120 AU). The disk at L' is bowed, as was seen at shorter wavelengths. This likely indicates the disk is not perfectly edge-on and contains highly forward scattering grains. Interior to ~ 50 AU, the surface brightness at L' rises sharply on both sides of the disk, which was also previously seen at Ks band. This evidence together points to the disk containing a second inner component located at $\lesssim$ 50 AU. Comparing the color of the outer (50 $< r$/AU $< 120$) portion of the disk at L' with archival HST/NICMOS images of the disk at 1-2 \microns allows us to test the recently proposed cometary grains model of Donaldson et al. 2013. We find that the model fails to match the disk's surface brightness and spectrum simultaneously (reduced chi-square = 17.9). When we modify the density distribution of the model disk, we obtain a better overall fit (reduced chi-square = 2.9). The best fit to all of the data is a pure water ice model (reduced chi-square = 1.06), but additional resolved imaging at 3.1 \microns is necessary to constrain how much (if any) water ice exists in the disk, which can then help refine the originally proposed cometary grains model.Comment: Accepted to ApJ January 13, 2014. 12 pages (emulateapj style), 9 figures, 1 tabl

Ethylene-mediated nitric oxide depletion pre-adapts plants to hypoxia stress

Timely perception of adverse environmental changes is critical for survival. Dynamic changes in gases are important cues for plants to sense environmental perturbations, such as submergence. In Arabidopsis thaliana, changes in oxygen and nitric oxide (NO) control the stability of ERFVII transcription factors. ERFVII proteolysis is regulated by the N-degron pathway and mediates adaptation to flooding-induced hypoxia. However, how plants detect and transduce early submergence signals remains elusive. Here we show that plants can rapidly detect submergence through passive ethylene entrapment and use this signal to pre-adapt to impending hypoxia. Ethylene can enhance ERFVII stability prior to hypoxia by increasing the NO-scavenger PHYTOGLOBIN1. This ethylene-mediated NO depletion and consequent ERFVII accumulation pre-adapts plants to survive subsequent hypoxia. Our results reveal the biological link between three gaseous signals for the regulation of flooding survival and identifies key regulatory targets for early stress perception that could be pivotal for developing flood-tolerant crops

QES-Fire: A dynamically coupled fast-response wildfire model

A microscale wildfire model, QES-Fire, that dynamically couples the fire front to microscale winds was developed using a simplified physics rate of spread (ROS) model, a kinematic plume-rise model and a mass-consistent wind solver. The model is three-dimensional and couples fire heat fluxes to the wind field while being more computationally efficient than other coupled models. The plume-rise model calculates a potential velocity field scaled by the ROS model\u27s fire heat flux. Distinct plumes are merged using a multiscale plume-merging methodology that can efficiently represent complex fire fronts. The plume velocity is then superimposed on the ambient winds and the wind solver enforces conservation of mass on the combined field, which is then fed into the ROS model and iterated on until convergence. QES-Fire\u27s ability to represent plume rise is evaluated by comparing its results with those from an atmospheric large-eddy simulation (LES) model. Additionally, the model is compared with data from the FireFlux II field experiment. QES-Fire agrees well with both the LES and field experiment data, with domain-integrated buoyancy fluxes differing by less than 17% between LES and QES-Fire and less than a 10% difference in the ROS between QES-Fire and FireFlux II data

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey : measuring DA and H at z = 0.57 from the baryon acoustic peak in the Data Release 9 spectroscopic Galaxy sample

We present measurements of the angular diameter distance to and Hubble parameter at z = 0.57 from the measurement of the baryon acoustic peak in the correlation of galaxies from the Sloan Digital Sky Survey III Baryon Oscillation Spectroscopic Survey. Our analysis is based on a sample from Data Release 9 of 264 283 galaxies over 3275 square degrees in the redshift range 0.43 < z < 0.70. We use two different methods to provide robust measurement of the acoustic peak position across and along the line of sight in order to measure the cosmological distance scale. We find DA(0.57) = 1408 ± 45 Mpc and H(0.57) = 92.9 ± 7.8 km s−1 Mpc−1 for our fiducial value of the sound horizon. These results from the anisotropic fitting are fully consistent with the analysis of the spherically averaged acoustic peak position presented in Anderson et al. Our distance measurements are a close match to the predictions of the standard cosmological model featuring a cosmological constant and zero spatial curvature.Publisher PDFPeer reviewe

Comparative genomics of Mycobacterium avium complex reveals signatures of environment-specific adaptation and community acquisition

Nontuberculous mycobacteria, including those in the Mycobacterium avium complex (MAC), constitute an increasingly urgent threat to global public health. Ubiquitous in soil and water worldwide, MAC members cause a diverse array of infections in humans and animals that are often multidrug resistant, intractable, and deadly. MAC lung disease is of particular concern and is now more prevalent than tuberculosis in many countries, including the United States. Although the clinical importance of these microorganisms continues to expand, our understanding of their genomic diversity is limited, hampering basic and translational studies alike. Here, we leveraged a unique collection of genomes to characterize MAC population structure, gene content, and within-host strain dynamics in unprecedented detail. We found that different MAC species encode distinct suites of biomedically relevant genes, including antibiotic resistance genes and virulence factors, which may influence their distinct clinical manifestations. We observed that M. avium isolates from different sources-human pulmonary infections, human disseminated infections, animals, and natural environments-are readily distinguished by their core and accessory genomes, by their patterns of horizontal gene transfer, and by numerous specific genes, including virulence factors. We identified highly similar MAC strains from distinct patients within and across two geographically distinct clinical cohorts, providing important insights into the reservoirs which seed community acquisition. We also discovered a novel MAC genomospecies in one of these cohorts. Collectively, our results provide key genomic context for these emerging pathogens and will facilitate future exploration of MAC ecology, evolution, and pathogenesis